AI Platform Engineer – MLOps, Cloud & Observability

Analog Devices, Inc.; Limerick; On-site; EUR 90,000 - 120,000; Full time

Job summary

Analog Devices, Inc. is seeking a Staff AI Platform Engineer to build and operate scalable AI platforms and tooling. You will own platform components, APIs, and integration patterns to accelerate AI adoption across the enterprise.

You'll work with senior engineers, shape architecture, and ensure cost-effective, reliable operations in a multi-cloud environment while advancing MLOps/LLMOps practices.

Qualifications

• Strong Python development skills with maintainable code and automation.
• Hands-on cloud experience with AWS, GCP, or Azure and cost-aware design.
• Experience building or operating internal platforms and tooling.
• Solid knowledge of containers, Docker, and CI/CD basics.
• Excellent communication and a service-minded approach.

Responsibilities

• Build and operate the AI platform and tooling used to develop, deploy, and monitor AI-powered apps.
• Create reusable components, APIs, and integration patterns for safer AI adoption.
• Own integrations with cloud model providers (Bedrock, Vertex AI) and cost management.
• Serve as a point of contact for internal users and translate needs into solutions.
• Enhance observability, logging, metrics, and tracing across multi-cloud environments.
• Uphold AI engineering standards for security, cost, and governance.

Skills

Python

Git

AWS

GCP

Azure

Docker

Observability

Cost awareness

Reliability

LLMOps

Communication

Education

Bachelor's degree in Computer Science

Tools

Terraform

Kubernetes

LangChain

Bedrock/Vertex AI

Job description

Analog Devices, Inc. is seeking a Staff AI Platform Engineer to build and operate scalable AI platforms and tooling. You will own platform components, APIs, and integration patterns to accelerate AI adoption across the enterprise.

You'll work with senior engineers, shape architecture, and ensure cost-effective, reliable operations in a multi-cloud environment while advancing MLOps/LLMOps practices.